Automatic Topic Identification Using Ontology Hierarchy

This paper proposes a method of using ontology hierarchy in automatic topic identification. The fundamental idea behind this work is to exploit an ontology hierarchical structure in order to find a topic of a text. The keywords which are extracted from a given text will be mapped onto their corresponding concepts in the ontology. By optimizing the corresponding concepts, we will pick a single node among the concepts nodes which we believe is the topic of the target text. However, a limited vocabulary problem is encountered while mapping the keywords onto their corresponding concepts. This situation forces us to extend the ontology by enriching each of its concepts with new concepts using the external linguistics knowledge-base (WordNet). Our intuition of a high number keywords mapped onto the ontology concepts is that our topic identification technique can perform at its best.

• Type: Conference
• Year: 2001
• Where: CICLING
• Authors: Sabrina Tiun, Rosni Abdullah, Tang Enya Kong
• Keywords: CICLING 2001, Corresponding Concepts, Natural Language Processing, Ontology, Ontology Hierarchical Structure